A robust data simulation technique to improve early detection performance of a classifier in control chart pattern recognition systems

Abstract

The quality control process is essential in maintaining the stability of production systems and proactively detecting abnormalities that may result in high mechanical and labor costs. In this study, a new data simulation strategy was devised to improve the early prediction performance (EPP) of an algorithm. Traditional control chart data simulation methods work based on creating abnormal patterns consisting of only abnormal signals. However, a classifier trained with data samples consisting of only abnormal data signals may fail to early detect abnormality in a real-time production line, in which abnormal signals is obscured by volume of normal signals. From this perspective, training a model by imitating real-world cases can improve the performance of an algorithm in terms of early detection of an abnormality. Normal and abnormal patterns were simulated by implementing a new approach called Mix Ratio Data Simulation (MRDS). The proposed methodology MRDS is compared with the customary data simulation method under the predefined scenarios in terms of EPP. The findings indicated that changing the way of simulating dataset increases the EEP of the machine-learning algorithm regardless of abnormality types and parameters.